This may appear to be an awkward time to present a model-data comparison. This post will appear between two excellent post at WattsUpWithThat, both of which discuss the flaws in the IPCC’s use of the multi-model ensemble mean. One post (here) is by Geoffrey H Sherrington and the other (here,) is by Robert G. Brown of Duke University—a.k.a. blogger rgbatduke. They are worth reading.

Regardless of the discussions taking place around the blogosphere, the IPCC and climate science community as a whole will continue to present hindcasts and projections using multi-model ensemble means. And my posts have shown and will continue to show that the climate models, using those model means, show no skill at hindcasting. If they show no skill at hindcasting, there’s no reason to believe their projections of future climate.

OVERVIEW

The temperature difference between daily maximum and minimum temperatures is known as the diurnal temperature range. The much-reported decrease in the diurnal temperature range has been said to be one of the “fingerprints” of human-induced global warming. Climate models, of course, were used for attribution studies to determine that manmade greenhouse gases were one of the causes. Then along came the Berkeley Earth Surface Temperature (BEST) data, which showed an increase in the diurnal temperature range since 1988. Oops!

This post is a model-data comparison of maximum (Tmax) and minimum (Tmin) temperature anomalies as presented by the BEST data and the multi-model ensemble mean of the climate models stored in the CMIP5 archive. Those climate models were prepared for the IPCC’s 5th Assessment Report. As one might suspect, the models did not perform well, and that’s putting it nicely.

If the decrease in diurnal temperature range is a “fingerprint” of human-induced global warming, climate scientists need a better AFIS.

Figure 1

INTRODUCTION

Easterling et al (1997) Maximum and Minimum TemperatureTrends for the Globe is an often-cited paper about the changes in theDaily Maximum (Tmax) and Minimum (Tmin) Temperatures and in the difference between the two, which is called Diurnal Temperature Range (DTR). They examined data for the period of 1950 to 1993. The abstract begins:

Analysis of the global mean surface air temperature has shown that its increase is due, at least in part, to differential changes in daily maximum and minimum temperatures, resulting in a narrowing of the diurnal temperature range (DTR).

Analyses of the year-month mean maximum and minimum surface thermometric record have now been updated and expanded to cover three large countries in the Northern Hemisphere (the contiguous United States, the Soviet Union, and the People’s Republic of China). They indicate that most of the warming which has occurred in these regions over the past four decades can be attributed to an increase of mean minimum (mostly nighttime) temperatures. Mean maximum (mostly daytime) temperatures display little or no warming. In the USA and the USSR (no access to data in China) similar characteristics are also reflected in the changes of extreme seasonal temperatures, e.g., increase of extreme minimum temperatures and little or no change in extreme maximum temperatures. The continuation of increasing minimum temperatures and little overall change of the maximum leads to a decrease of the mean (and extreme) temperature range, an important measure of climate variability.

The cause(s) of the asymmetric diurnal changes are uncertain, but there is some evidence to suggest that changes in cloud cover plays a direct role (where increases in cloudiness result in reduced maximum and higher minimum temperatures). Regardless of the exact cause(s), these results imply that either: (1) climate model projections considering the expected change in the diurnal temperature range with increased levels of the greenhouse gases are underestimating (overestimating) the rise of the daily minimum (maximum) relative to the maximum (minimum), or (2) the observed warming in a considerable portion of the Northern Hemisphere landmass is significantly affected by factors unrelated to an enhanced anthropogenically-induced greenhouse effect.

The usefulness of global-average diurnal temperature range (DTR) as an index of climate change and variability is evaluated using observations and climate model simulations representing unforced climate variability and anthropogenic climate change. On decadal timescales, modelled and observed intrinsic variability of DTR compare well and are independent of variations in global mean temperature. Observed reductions in DTR over the last century are large and unlikely to be due to natural variability alone.

We use a global climate model to investigate the impact of a wide range of radiative forcing and feedback mechanisms on the diurnal cycle of surface air temperature. This allows us not only to rule out many potential explanations for observed diurnal changes, but to infer fundamental information concerning the nature and location of the principal global climate forcings of this century. We conclude that the observed changes of the diurnal cycle result neither from natural climate variability nor a globally-distributed forcing, but rather they require the combination of a (negative) radiative forcing located primarily over continental regions together with the known globally-distributed forcing due to anthropogenic greenhouse gases.

The twentieth-century warming has been accompanied by a decrease in those areas of the world affected by exceptionally cool temperatures and to a lesser extent by increases in areas affected by exceptionally warm temperatures. In recent decades there have been much greater increases in night minimum temperatures than in day maximum temperatures, so that over 1950–1993 the diurnal temperature range has decreased by 0.088C per decade.

But as shown in Figure 1 above, it’s obvious in the Berkeley Earth Surface Temperature (BEST) Daily Maximum (Tmax) and Minimum (Tmin) Temperature data that the diurnal temperature range has been increasing over the past 2+ decades, not decreasing.

Some of the climate models predict that the diurnal temperature range, that is, the difference between Tmax and Tmin, should decrease due to greenhouse warming. The physics is that greenhouse gases have more impact at night when they absorb infrared and reduce the cooling, and that this effect is larger than the additional daytime warming. This predicted change is sometimes cited as one of the “fingerprints” that separates greenhouse warming from other effects such as solar variability. Previous studies [15-18] reported significant decreases in the diurnal temperature range over the period 1948 to 1994. Jones et al. [18] for example described the decrease as 0.08°C per decade for the period 1950 to 1993.

The result of this calculation is shown in figure 4. The solid line represents the annual average of the diurnal range, and the dashed line shows the 10-year running average. The 1- and 2-standard deviation error uncertainties are shown with the two grey bands for the 10-year average. The behavior of the diurnal range is not simple; it drops from 1900 to 1987, and then it rises. The rise takes place during a period when, according to the IPCC report, the anthropogenic effect of global warming is evident above the background variations from natural causes.

Although the post-1987 rise is not sufficient to undo the drop that took place from 1901 to 1987, the trend of 0.86 ± 0.13°C/century is distinctly upwards with a very high level of confidence. This reversal is particularly odd since it occurs during a period when the rise in Tavg was strong and showed no apparent changes in behavior.

Hmm. Rohde et al seem to be throwing a few jabs at the IPCC and the previous papers. Let’s see if those jabs are deserved.

MODEL-DATA COMPARISON OVERVIEW

As noted in the overview, this post compares the Berkeley Land Surface Air Temperature (BEST) data to the multi-model ensemble mean of the climate models prepared for the upcoming 5th Assessment Report of the Intergovernmental Panel on Climate Change. We’ll compare the daily maximum and minimum temperature data and the differences between the two. The source of the data and model outputs is the KNMI Climate Explorer. There, the BEST daily minimum and maximum data are presented only as anomalies, not in absolute form. As a result, I cannot present the modeled and observed diurnal temperature ranges in time-series graphs. That’s fine. We’ll present the differences in the trends of modeled and observed daily maximums and minimums.

We’ll break the comparisons into two parts, based on the dividing year identified by Rohde et al (2012). The first group of comparisons will be for the period of 1988 to 2011, which is the period when Rohde et al found an increase in the diurnal temperature range. For the second group, ending in 1987, we’ll use the start year of 1950 for two reasons. First, based on the Rohde et al Figure 4 (my Figure 2), the data uncertainties grow quite large before 1950. Second, I wanted the mid-20th Century cooling period to be a significant portion of the time period.

I’ve used the WMO-recommended base years of 1981-2010 for anomalies in the time-series graphs.

Because the IPCC is moving into regional climate projections in AR5, we’ll also compare the modeled and data linear trends on zonal-mean (latitude average) bases. With these trend comparisons, the fact that data is presented as anomalies becomes a moot point, and as a result, we can present the trends in the diurnal temperature ranges.

MODEL-DATA COMPARISON – 1988 TO 2011 – TIME SERIES

Figure 3 is a time-series graph of the BEST maximum and minimum temperature anomalies for the period of January 1988 to December 2011. This confirms the discussion from Rohde et al (2012). The maximum temperatures are warming a faster rate than the minimums. Based on the differences in the linear trends, the diurnal temperature range increased at a rate of about 0.09 Deg C/decade from 1988 to 2011.

Figure 3

On the other hand, the multi-model mean of the CMIP5-archived models the minimums and maximums are warming at basically the same rates from 1988 to 2011, with minimums warming at a slightly higher rate. See Figure 4. According to the models, if anthropogenic greenhouse gases and aerosols were the cause of the trends in the diurnal temperature range, there should have been minor decrease, not a significant increase, during this period.

Figure 4

MODEL-DATA COMPARISON – 1988 TO 2011 – TRENDS ON ZONAL MEAN BASIS

The graphs in this section present the warming rates based on linear trends for the period of January 1988 to December 2011. The (vertical) y-axis is scaled in deg C/Decade. The (horizontal) x-axis is scaled in degrees latitude. “-90” or 90S to the left is the South Pole, “0” is the equator, and “90” or 90N is the North Pole. Looking at Figure 5, the model-mean output of 0.25 deg C/decade at zero latitude indicates the models simulated that the maximum temperatures at the equator should have warmed at a rate of 0.25 deg C per decade from 1988 to 2011. To create the graph, the data and model outputs are first downloaded in 5-degree latitude bands. For example, the data point at -52.5 deg latitude includes the data from 55S-50S, and the data point at -47.5 deg latitude is for the data from 50S-45S. Then the linear trends (the warming rates) in deg C/decade are determined by the spreadsheet software.

Figure 5

Now let’s compare the models to the observations in Figure 5. The models overestimate the rate of warming in maximum temperatures from about 45S to 5N and from 35N to the Arctic. The models underestimate the rate of warming slightly from 5N to 35N, but grossly underestimate the polar-amplified warming in the Arctic. Hmm. We’re often told that polar amplification is consistent with climate model projections, while, in reality, the multi-model mean shows little polar amplification by comparison to the data.

The failure of the models to simulate the polar amplified warming should come as no surprise to regular visitors. We showed in the post here last year that the models do not show the polar-amplified warming in the Arctic during the recent warming period, or the polar-amplified cooling in the Arctic during the cooling period from the early-1940s to the late-1970s.

Figure 6 illustrates the modeled and observed warming rates in minimum temperatures from 1988 to 2011. The models overestimate the warming rates in minimum temperatures from South America to the Arctic, but again fail to capture the polar-amplified warming in the Arctic. Note also that the observed minimum temperatures show little warming in the extratropics of the Southern Hemisphere. The observed warming rates of the minimums increase gradually until just north of the equator and then remain relatively constant until about 57N, where the polar amplification kicks in.

Figure 6

I’m providing Figures 7 and 8 as references for Figure 9. Figure 7 includes the observed warming rates in maximum and minimum temperatures from 1988 to 2011, and Figure 8 compares the modeled maximum and minimum warming rates.

Figure 7

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Figure 8

Figure 9 presents the observed and models trends in the diurnal temperature ranges on zonal-mean bases. They were calculated as the differences between the observed and modeled trends in the maximum and minimum temperatures. Based on Figures 3 and 4, we already knew that the models failed to simulate the observed rates of warming as shown in those time-series graphs. So the intent here is really to show that the trends in the observed diurnal temperature ranges are not the same around the globe. Keep in mind that a positive trend in the diurnal temperature range indicates the maximum temperatures are warming faster than the minimums—and vice versa for a negative trend in the diurnal temperature range.

Figure 9

MODEL-DATA COMPARISON – 1950 TO 1987 – TIME SERIES

Figures 10 and 11 present the observed and modeled maximum and minimum temperatures (as anomalies) for the period of 1950 to 1987. Both the models and observations show minimums warming faster than the observations. But the models are showing that, if anthropogenic factors were responsible for the additional warming of the minimum temperatures, then the rate of warming should have been much less than the rates observed. That is, the models are showing a decrease in the diurnal temperature range that’s less than one fifth of the observed rate.

Figure 10

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Figure 11

MODEL-DATA COMPARISON – 1950 TO 1987 – TRENDS ON ZONAL MEAN BASIS

Figure 12 presents the trends in the maximum temperatures from 1950 to 1987 on a latitudinal basis. The models perform reasonable well until the extreme high latitudes of the Northern Hemisphere, where temperatures cooled over this period.

Figure 12

For the trends in the minimum temperatures from 1950 to 1987, Figure 13, the modeled warming rates were consistently higher than the observed trends—until they reach the Arctic, where, again, minimum temperatures cooled while the models say they should have warmed.

Figure 13

Figures 14 and 15 serve as references for Figure 16. Figure 14 includes the observed warming rates in maximum and minimum temperatures from 1950 to 1987, and Figure 15 compares the modeled maximum and minimum warming rates.

Figure 14

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Figure 15

Figure 16 shows the difference between the modeled and observed trends in diurnal temperature range for the period of 1950 to 1987. There is only a small region at the mid-latitudes of the Northern Hemisphere where the modeled trends in diurnal temperature range come close the observations.

Figure 16

One last graph: Figure 17 is for those readers who want to compare the modeled and observed trends in diurnal temperature ranges for the periods of 1950 to 1987 and 1988 to 2011.

Figure 17

That’s a very poor showing over both time periods, especially when one considers that the IPCC is moving toward regional modeling over decadal and multidecadal periods.

Why are they even bothering, other than to throw away billions in taxpayer dollars? Climate science is still in its infancy. And so far there has been little to no return on the investment. No ifs, ands or buts about that.

STANDARD BLURB ABOUT THE USE OF THE MODEL MEAN

We’ve published numerous posts that include model-data comparisons. If history repeats itself, proponents of manmade global warming will complain in comments that I’ve only presented the model mean in the above graphs and not the full ensemble. In an effort to suppress their need to complain once again, I’ve borrowed parts of the discussion from the post Blog Memo to John Hockenberry Regarding PBS Report “Climate of Doubt”.

The model mean provides the best representation of the manmade greenhouse gas-driven scenario—not the individual model runs, which contain noise created by the models. For this, I’ll provide two references:

The first is a comment made by Gavin Schmidt (climatologist and climate modeler at the NASA Goddard Institute for Space Studies—GISS). He is one of the contributors to the website RealClimate. The following quotes are from the thread of the RealClimate post Decadal predictions. At comment 49, dated 30 Sep 2009 at 6:18 AM, a blogger posed this question:

If a single simulation is not a good predictor of reality how can the average of many simulations, each of which is a poor predictor of reality, be a better predictor, or indeed claim to have any residual of reality?

Gavin Schmidt replied with a general discussion of models:

Any single realisation can be thought of as being made up of two components – a forced signal and a random realisation of the internal variability (‘noise’). By definition the random component will uncorrelated across different realisations and when you average together many examples you get the forced component (i.e. the ensemble mean).

To paraphrase Gavin Schmidt, we’re not interested in the random component (noise) inherent in the individual simulations; we’re interested in the forced component, which represents the modeler’s best guess of the effects of manmade greenhouse gases on the variable being simulated.

The quote by Gavin Schmidt is supported by a similar statement from the National Center for Atmospheric Research (NCAR). I’ve quoted the following in numerous blog posts and in my recently published ebook. Sometime over the past few months, NCAR elected to remove that educational webpage from its website. Luckily the Wayback Machine has a copy. NCAR wrote on that FAQ webpage that had been part of an introductory discussion about climate models (my boldface):

Averaging over a multi-member ensemble of model climate runs gives a measure of the average model response to the forcings imposed on the model. Unless you are interested in a particular ensemble member where the initial conditions make a difference in your work, averaging of several ensemble members will give you best representation of a scenario.

In summary, we are definitely not interested in the models’ internally created noise, and we are not interested in the results of individual responses of ensemble members to initial conditions. So, in the graphs, we exclude the visual noise of the individual ensemble members and present only the model mean, because the model mean is the best representation of how the models are programmed and tuned to respond to manmade greenhouse gases.

CLOSING

We can add maximum and minimum temperatures and diurnal temperature ranges to the variables that the IPCC’s climate models cannot simulate. In recent months we’ve also illustrated and discussed that the climate models stored in the CMIP5 archive for the upcoming 5th Assessment Report (AR5) cannot simulate observed:

I have a funny feeling I’ll be publishing another book. It will be relatively short, easy to understand and contain lots of graphs. I suspect the release date will be around the time that newspapers are heralding the gathering of politicians who’ve assembled to finalize the content of the Summary for Policymakers of the IPCC’s 5th Assessment Report. This time, I’ll release the book right from the get go in Amazon Kindle form, under their KDP select program. That way it’s free initially.

Maybe you can help with the title. My thoughts are to start with If You Can Read a Graph… But the best remainder I can come up with is …Why Do You Believe the IPCC? The subtitle: The IPCC’s Climate Models Show No Skill Simulating the Past. Why Do You Believe Their Projections of the Future?

Don’t most of the Latitude graphs show that there has been a shift of thermal energy from the southern to the northern hemispheres?

The Earth is a giant heat (energy) redistribution machine. The amount of warming or cooling that results, though, depends on what is being heated (cooled), due to the different thermal capacities of air, ground, ice, water, rock, dirt and human habitations (which is the basis of UHIE, after all). Regional effects are dominant, just as regional differences are dominant. The Earth is not responding consistently across all latitudinal and geographic areas as it should if the fundamental thing CO2 does is heat the atmosphere which SUBSEQUENTLY heats everything else.

The warming of the Arctic: warm water coming through the Bering Straits hits the coastal communities before it hits the eastern Arctic (as the oceanic currents go west to east, though there is a circular thing going on as well). The time difference in North Alaskan changes and those of Baffin Island reflect the movement of warming water, not the delayed effect of atmospheric warming.

I don’t believe anyone has a problem with model means – this is clearly the correct way to average out the noise from individual runs.
Averaging between different models, however, is loony, as RGB has pointed out so well.

Which is annoyingly exactly what I wanted to say. All I can add is the emphasis that I agree.

Averaging simulations of the same physics is A-OK.
Averaging simulations of different physics is just drawing lines with an over-priced Etch-a-Sketch.

Thanks, Bob. Another killer article!
I also like to point out that the UHI rises the minimum temperatures at night and almost not the maximum temperatures in daytime.
I can visualize a book named “If You Can Read a Graph…”, subtitled “…Why Do You Believe the IPCC?”.

Very interesting Bob. I’ll have to read it a few times to properly digest.

However, I need to point out, the error Karl and other climate scientists make.

They indicate that most of the warming which has occurred in these regions over the past four decades can be attributed to an increase of mean minimum (mostly nighttime) temperatures. …

The cause(s) of the asymmetric diurnal changes are uncertain, but there is some evidence to suggest that changes in cloud cover plays a direct role (where increases in cloudiness result in reduced maximum and higher minimum temperatures).

The error that Karl others make, is that because minimum temperatures are strongly influenced by nighttime temperatures, they then assume changes in minimum temperatures are also strongly influenced by changes in nighttime temperatures.

Changes in minimum temperatures are also strongly influenced by factors that affect early morning solar radiation , particularly low level clouds. And there is good evidence that early morning solar insolation changes (resulting from low level aerosol seeded clouds changes) are the main reason for increasing minimum temperatures.

So when Karl says the causes are ‘uncertain’, he means the data doesn’t support the AGW theory’s reason for increasing minimums.

Bob, I think you also need to consider the Hansen effect. While he was at GISS, Hansen so screwed with / adjusted the instrumental readings to the point that a comparison using the Tmin and the Arctic temperture record may (?!) be unreliable.

It appears to me that the increase in diurnal range is probably some temporary cyclic thing, like the lesser increase that was centered around the ~1940 peak of global temperatures.

Although CO2 is modeled to decrease diurnal range (the lapse rate negative feedback applies more to max than to min temperatures), growth of the urban heat island effect also decreases diurnal range. An increase in reported diurnal range would be negative evidence for both CO2 causing warming-as-modeled and for reported warming being contaminated by growth of urban heat island effects.

I found that when the NCDC raw data is averaged across the year, todays temp increase minus tonights drop are almost identical, and average 17-18F. No real trends, no evidence of any loss of cooling.
In part I wanted to use raw data because of the unholy trashing of the data, what you found just confirms my worse fears.

Mike Crow brought this up on a May 17 post, and got me thinking. First point, the atmosphere doesn’t lose much heat over the course of a day. Any differences in day-night background radiation would probably be due to changes in cloud cover.

So the atmosphere as a whole cools by less than 1% over the course of a day. That figure makes sense when you figure that the earth’s surface temperature may change by 10 C or more overnight far more than average changes over a week, but weather patterns persist for several days, and that’s why meteorologists can predict daily highs out a week or so. That cooling is obviously mostly from the
earth’s surface and air near the surface ,leaving most of the atmosphere unchanged.

For the earth, we get an average of 480 watts from the sun and 250 watts from the atmosphere during the day, when it is warming, and get 0 watts from the sun and 250 watts from the atmosphere at night, when it is cooling.

On day-night temperature differences:

Suppose there’s a change in temp due solely to greenhouse gases, say an increase to 300 wats.
Then the earth will be receiving an average 480 watts from the sun and 300 from the atmosphere during the daytime, and 300 from the atmosphere during the nighttime.
Temperature is proportional to the 4th root of wattage, the ratio of
(480 + 250)//(250 is greater than (480 + 300)/(300) so as the the difference between daytime highs and nighttime lows should drop with an increased greenhouse effect, rise with at decreased greenhouse effect.

If it;s the sun, changes in temperature of the sun can effect the distribution of the sun;s spectrum, and the fraction of sunlight reflected, absorbed by the atmosphere, and hitting the earth’s surface. To simplify, letting the sun’s temperature stay the same, but letting
its luminosity increase- I suppose the greenhouse magnifier would act proportionally the same as it does now.
With a 5% incrrease in the sun’s luminosity, wed get 1.05 times 480 watts from the sun during the day, 1.05 times 250 watts from the atmosphere both during the day and during the night, ,
and the RATIO of day to nighttime temps would stay the same, but with ABSOLUTE warming, the
DIFFERENCE between day and nighttime temps should INCREASE with increased solar luminosity.

It looks like real life figures for the last 20 years match the sun model rather than the greenhouse gas model

Yes, I think you’re right, the median temp doesn’t change much in a week. And it’s direction (warming/cooling) is controlled by the ratio of length of day vs night. Which is what I think you’d expect. I also think this goes with what’s driving temps, the air masses coming in from the oceans, why the AMO/PDO has such a large effect, its setting the base point that the days/seasons temps cycles around.

You mention DLR of 250 w/sq m. This could be an average, and my method could give the wrong answer, though I can’t think why. I’ve used a low temp (-70F) IR thermometer to measure the zenith, on a clear 35F January day, it measured ~-40F, which works out to almost 160w/sq m. At 35F there would be very little water vapor, and on a humid day I would expect it to be much high, from water not Co2 though. But as I said, 250 could be a good average value, but my sample size is of 1 location a couple of times. I hope to put up a data logging IR sensor to go with my weather station sometime this year.

Any differences in day-night background radiation would probably be due to changes in cloud cover.

I also agree with this, even if co2 has “warmed” the planet, clouds regulate surface change, which is why my “Rise” and “Fall” are both the same, and well regulated for a wide range of weather effects. As I mentioned I was really astonished at how close they where. I did a sum of rise/fall, I don’t remember how long the period was(maybe a month of station records), but I remember the sum was 4 million and some, and there was only 17F degrees difference between the two, 17 out of 4 some million.

That last one is my best so far. It’s short, intriguing, and allusive. Maybe it could be tweaked a bit, thusly:
Change “Should” to “Don’t Let” & drop the “?”
Or change to:Super Models Can Belie Your Eyes–Beware
(“Beware” is optional.)

Hi Bob. Yes I’m having fun. My repeated revisions of my suggestions eventually led me to an acceptable title last time around (“Who Turned on the Heat?”). I think I’ve just about tweaked my to perfection again. Here’s my latest–the added word “Duper” subtly connotes deception:

That tweak gives “Duper” the prominence it needs to suggest decption; and it gets “Super” closer to “Model,” suggesting something that’s glitzy and bedazzling but shallow. I like it. it’s got a couple of rhymes, a couple of interest-building variations on normality (“Duper Super” and the unusual word “Belie”), and an allusion to the well-known ridiculous rank-pulling phrase of Groucho’s, “Who do you believe, me or your lying eyes?” It’ll give the reader’s mind something to chew over, and suggest that the body of the work is similarly “meaty.”

If you can read a graph … the evidence supporting dangerous climate change vanishes.

BTW, there’s a good reason they use ensemble means. It’s really quite simple. Those means give a better answer than anything else. Of course, the fact the ensemble mean is meaningless isn’t very important to the propagandists.

Here’s why they give a better answer. As each model is built it misses reality because of all the unknowns. The modelers then try and correct for the past. Each correction is different. When you combine a bunch of models those corrections look random. They take on the same attributes as does random noise.

An analogy. Take a very poor dart thrower. After missing the target the next toss will try and factor in the miss. Instead of correcting for bad technique the thrower aims differently. Now, take a bunch of dart throwers. Some will miss left, some right and others up or down. Hence, the corrections added together tend to cancel each other out.

With models the averaging tends to eliminate the worthless corrections. While the answer is still wrong at least it isn’t very, very wrong. What you end up with is the beliefs that are common among most of the modelers. The fact this method fails so badly demonstrates quite well that the basic assumptions common to all models are wrong.

we get an average of 342 watts from the sun, 107 reflected by clouds. I rounded to 240 watts,
avraged double that for daytime, zero for night.
The ground gets 161 direct from the sun plus 333 back radiation, which rounds to 490 watts.+
Taking the 240 watts from the sun, that leaves 250 from back radiation, 150 in direct sensible heat, the other 100 in evaporation and convection